Kristof Csorba

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to classify the droplets in the correct category, which is correlated with the viscosity of the sample. Different solutions samples were created using different ratios of Water and PVP (Polyvinylpyrrolidone) and videos of their droplets were taken and processed. It was found that for water-PVP solutions, the proposed ANN model was able to successfully classify the droplets using the data extracted from the videos with high accuracy. The results imply that the ANN model can recognize the features that affect the viscosity values.

Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR.

A novel topic based document clustering technique is presented in the paper for situations, where there is no need to assign all the documents to the clusters. Under such conditions the clustering system can provide a much cleaner result by rejecting the classification of documents with ambiguous topic. This is achieved by applying a confidence measurement for every classification result and by discarding documents with a confidence value less than a predefined lower limit. This means that our system returns the classification for a document only if it feels sure about it If not, the document is marked as "unsure". Beside this ability the confidence measurement allows the use of a much stronger term filtering, performed by a novel, supervised term cluster creation and term filtering algorithm, which is presented in this paper as well.